System and method using augmented reality for efficient collection of training data for machine learning

ABSTRACT

One embodiment provides a system that facilitates efficient collection of training data. During operation, the system obtains, by a recording device, a first image of a physical object in a scene which is associated with a three-dimensional (3D) world coordinate frame. The system marks, on the first image, a plurality of vertices associated with the physical object, wherein a vertex has 3D coordinates based on the 3D world coordinate frame. The system obtains a plurality of second images of the physical object in the scene while changing one or more characteristics of the scene. The system projects the marked vertices on to a respective second image to indicate a two-dimensional (2D) bounding area associated with the physical object.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/826,588, Attorney Docket Number PARC-20170647US02, entitled “SYSTEMAND METHOD USING AUGMENTED REALITY FOR EFFICIENT COLLECTION OF TRAININGDATA FOR MACHINE LEARNING,” by inventors Matthew A. Shreve, SricharanKallur Palli Kumar, Jin Sun, Gaurang R. Gavai, Robert R. Price, and HodaM. A. Eldardiry, filed 29 Nov. 2017 (hereinafter the “0647U502application”), the disclosure of which is incorporated by referenceherein.

The 0647US02 application claims the benefit and priority of U.S.Provisional Application No. 62/579,000, Attorney Docket NumberPARC-20170647US01, entitled “SYSTEM AND METHOD USING AUGMENTED REALITYFOR EFFICIENT COLLECTION OF TRAINING DATA FOR MACHINE LEARNING,” byinventors Matthew A. Shreve, Sricharan Kallur Palli Kumar, Jin Sun,Gaurang R. Gavai, Robert R. Price, and Hoda M. A. Eldardiry, filed 30Oct. 2017, the disclosure of which is incorporated by reference herein.

BACKGROUND Field

This disclosure is generally related to computer vision systems. Morespecifically, this disclosure is related to a system and method usingaugmented reality (AR) for efficient collection of training data formachine learning.

Related Art

Currently, training a computer vision system is accomplished through atedious process of manually collecting many images or videos. A humanexpert is subsequently needed to annotate or label the object ofinterest in each of the images or video frames. This inefficient processmay result in high costs due to the significant amount of time requiredas well as an increased error rate in labeling based on human fatigue.

Various efforts have been made to reduce the burden of manual labeling,including the development of human computer interfaces that allowefficient parsing of images and drawing of bounding boxes, and the useof technology that provides pointers that users can then modify insteadof specifying them from scratch. For example, a Kalman filter may beused to predict or interpolate the location of an object in a videobased on its past trajectory, with a human adjusting these predictionsas needed. However, these methods still require significant manual laborand do not provide a significant reduction in the labeling processnecessary for collecting training data.

SUMMARY

One embodiment provides a system that facilitates efficient collectionof training data. During operation, the system obtains, by a recordingdevice, a first image of a physical object in a scene which isassociated with a three-dimensional (3D) world coordinate frame. Thesystem marks, on the first image, a plurality of vertices associatedwith the physical object, wherein a vertex has 3D coordinates based onthe 3D world coordinate frame. The system obtains a plurality of secondimages of the physical object in the scene while changing one or morecharacteristics of the scene. The system projects the marked vertices onto a respective second image to indicate a two-dimensional (2D) boundingarea associated with the physical object.

In some embodiments, the marked plurality of vertices corresponds to oneor more regions of interest on the physical object. Projecting themarked vertices further comprises indicating a 2D bounding areaassociated with the one or more regions of interest on the physicalobject.

In some embodiments, the marked plurality of vertices can indicate oneor more of: a polygon; a portion of a surface plane; and a volume.

In some embodiments, marking the plurality of vertices furthercomprises: determining how to indicate the 2D bounding area of theprojected marked vertices on the respective second image.

In some embodiments, the 2D bounding area and the respective secondimage are presented on a display associated with the recording device,and the 2D bounding area indicates a 2D shape or a 3D volume.

In some embodiments, the 2D bounding area is indicated by one or moreof: a type, pattern, or color of a connector between the projectedvertices in the respective second image; a color, shading, or fill of ashape formed by connecting the projected vertices in the respectivesecond image; text describing the 2D bounding area; and an indication ofa label or description for the 2D bounding area.

In some embodiments, a 2D bounding area corresponds to a characteristicof the scene.

In some embodiments, a characteristic of the scene is one or more of: apose of the recording device; a lighting of the scene; a distance of therecording device from the physical object; an orientation of therecording device in relation to the physical object; a background of thephysical object or the scene; and an occlusion of one or more portionsof the physical object.

In some embodiments, the system stores, in a collection of trainingdata, the first image with the marked plurality of vertices. The systemstores, in the collection of training data, the plurality of secondimages with the projected marked vertices. The system trains, based onthe collection of training data, a convolutional neural network toidentify features of the physical object. The system deploys the trainednetwork on a mobile computing device to identify the features of thephysical object.

In some embodiments, the recording device includes one or more of: anaugmented reality device; a virtual reality device; a device withmagnetic sensors which determine 3D coordinates for a vertex in the 3Dworld coordinate frame; a camera and a hand-tracking sensor; a camerawhich records red, green, and blue (RGB), wherein the hand-trackingsensor determines 3D coordinates for a vertex in the 3D world coordinateframe; a camera which records red, green, and blue (RGB), and a 3Dsensor which records a depth; a device which records images or video,and determines 3D coordinates for a vertex in the 3D world coordinateframe based on visual cues or a position-sensing technology; and adevice which records images or video and includes a (3D) sensor.

BRIEF DESCRIPTION OF THE FIGURES

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 illustrates an exemplary environment for facilitating efficientcollection of training data, in accordance with an embodiment of thepresent invention.

FIG. 2 presents a flowchart illustrating a method for facilitatingefficient collection of training data, in accordance with an embodimentof the present application.

FIG. 3 illustrates a human using an AR device to mark corners of systemfeatures, in accordance with an embodiment of the present invention.

FIG. 4A illustrates an example of creating a bounding box of a printerfrom a first perspective using an AR device, in accordance with anembodiment of the present invention.

FIG. 4B illustrates an example of creating a bounding box of a printerfrom a second perspective using an AR device, in accordance with anembodiment of the present invention.

FIG. 5A illustrates an example of creating a bounding box of printerparts from a first perspective using an AR device, in accordance with anembodiment of the present invention.

FIG. 5B illustrates an example of creating a bounding box of printerparts from a second perspective using an AR device, in accordance withan embodiment of the present invention.

FIG. 6A illustrates a labeling interface that can be used with MicrosoftHoloLens, in accordance with an embodiment of the present invention.

FIG. 6B illustrates the labeling interface of FIG. 6A without thesurface meshes, in accordance with an embodiment of the presentinvention.

FIG. 7A illustrates an example of automatically generatedtwo-dimensional marker locations and the corresponding bounding boxesfrom a first perspective using the Microsoft HoloLens interface, inaccordance with an embodiment of the present invention.

FIG. 7B illustrates an example of automatically generatedtwo-dimensional marker locations and the corresponding bounding boxesfrom a second perspective using the Microsoft HoloLens interface, inaccordance with an embodiment of the present invention.

FIG. 7C illustrates an example of automatically generatedtwo-dimensional marker locations and the corresponding bounding boxesfrom a second perspective using the Microsoft HoloLens interface, inaccordance with an embodiment of the present invention.

FIG. 8 illustrates an exemplary computer and communication system thatfacilitates efficient collection of training data, in accordance with anembodiment of the present invention.

In the figures, like reference numerals refer to the same figureelements.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the embodiments, and is provided in the contextof a particular application and its requirements. Various modificationsto the disclosed embodiments will be readily apparent to those skilledin the art, and the general principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the present disclosure. Thus, the present invention is notlimited to the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features disclosed herein.

Overview

The embodiments described herein solve the problem of reducing theburden of manual labeling of training data by providing a system whichallows a user to efficiently collect training data. The system allows ahuman to use a recording device to capture and annotate an object ofinterest in a single image. The system subsequently projects theannotations onto the same object of interest in many other similarimages under varying conditions in the same environment.

Specifically, the user can use the recording device to capture images orvideos of a physical object in an environment (e.g., a “scene”) which isassociated with a three-dimensional (3D) world coordinate frame. Usingthe recording device on a single image or video frame, the user can markpoints on the physical object. A point can be a vertex with 3Dcoordinates based on the 3D world coordinate frame. Determining the 3Dcoordinates of a vertex may be based on visual cues or otherposition-sensing technologies that define a physical object pose in ascene. When the scene is changed (e.g., the user and the recordingdevice move to a different location in the room relative to the physicalobject), the system can display to the user (on images or video capturedunder the changed conditions) the marked points projected to indicate abounding box associated with the physical object.

For example, the recording device can be a camera with 3D-trackingsensors. The scene can be an office and the physical object of interestcan be a printer in the office. The system can define the camera pose inthe scene (i.e., the 3D world coordinate frame), while the user marksthe vertices to indicate a region of interest on the physical object(e.g., annotates or labels a printer output tray on the printer). Thesystem can track the marked vertices while varying one or morecharacteristics of the scene (e.g., changing the camera location,orientation, or pose).

The system can then project the marked vertices into the camera frame atdifferent camera poses, where each projection is an automaticallyannotated image which can be used as part of the collected (and labeled)training data. That is, given other images of the printer take fromdifferent camera poses, the system can project the marked vertices ontothe other images to indicate a bounding area around the region ofinterest on the printer, thus automatically annotating the other imagesbased solely on the single annotated image. The bounding area can be abounding box or a label which corresponds to a characteristic of thescene.

By using a recording device with a 3D sensor (or other position-sensingtechnology), the system allows a user to easily label the location,extent, pose or other properties of an object of interest once within anenvironment, and then project this labeling to a very large number ofimages or video frames taken under various conditions within the sameenvironment. This process can generate, with little effort from theuser, a large number of images or video frames of an object of interestunder various conditions, where each image or frame is labeled withproperties projected from the original environment.

Thus, the embodiments described herein provide a system which increasesthe efficiency collecting training data for machine learning. Theincreased efficiency can include a significant reduction in the amountof manual labeling required to annotate images, including multipleregions or objects of interest in the images. Because effective machinelearning is based on the diversity of training data, and because thesystem efficiently collects diverse training data, the embodimentsdescribed herein improve the technological field of machine learning.

Exemplary Embodiments

In one exemplary embodiment, a user can use the Microsoft HoloLenssystem which uses an RGB-D (red green blue plus depth) camera plus aSimultaneous Localization and Mapping (SLAM) style algorithm to build amodel of the room. The user can use the HoloLens to place virtualmarkers in the 3D model of the room to define the location and extent ofan object of interest within the room. The user can take a number ofpictures of the object from different views with the HoloLens, underdifferent lighting conditions, at different distances and orientations,and in the presence of occluding objects. The HoloLens can calculate itspose within the 3D model of the room for each image.

The system can project the user's original markers from the 3D model ofthe room into each image to form a bounding box for the object ofinterest. These automatically annotated images can be used to trainobject detection and recognition systems employing technologies such asdeep neural networks.

In another exemplary embodiment, a device that can track and record theposition of hand movements can be used to annotate real-world objects.For example, the tracked hand-controllers included with the HTC Vive(which uses a NIR base station to track multiple sensors in thecontroller) can be used to trace boundaries of the real-world objectsusing a trigger input on the controller. In conjunction with a secondcalibrated camera (e.g., the embedded camera in the HTC Vive or anexternally co-calibrated “mixed-reality” camera), objects can be imagedfrom different perspectives with properly aligned boundary annotations.

In a further exemplary embodiment, a device with magnetic sensors can beused to annotate and track the 3D coordinates. For example, a 3D motiontracking system by Polhemus can track the position and orientation of asensor (e.g., on the recording device) as it moves through space byusing electro-magnetic fields to determine the position and orientationof a remote object (e.g., the real-world object).

Improvements to Existing Technologies

Some common forms of augmented reality (AR) in the service industryinclude a tethered telepresence, a visual retrieval of information, anda repair script with overlays. However, each of these results ishindered by inefficiencies. In a tethered telepresence, a remotetechnician may need to perform a visual inspection, which can requireboth connectivity and extensive human expert time. In a visual retrievalof information (e.g., the Digital Glove Box application), a camera mayretrieve a model number, manual pages, or telemetry statistics. However,the output is a passive page and may be limited to a single room. In arepair script with overlays, a checklist or procedural prompt may beoverlaid on a user's view, and the user can click through the overlaidview. However, the view may be expensive to create and is still mostlypassive, in that the view is not able to understand the user's actions.Thus, producing stand-alone AR content currently requires extensive work(e.g., by artists, human experts, and machine learning experts) tocreate images and video (e.g., animation) to train a model, which canresult in an inefficient system.

The embodiments described herein provide a system which increases theefficiency of collecting training data for machine learning. In additionto decreasing the amount of manual time and labor required to collecttraining data, the system can also decrease the development time of newobject detection systems. Beyond bounding box coordinates, the groundtruth annotation can also capture 3D information about the objectlocation, orientation, and pose from the recording device. The collecteddata can thus be used for a wider set of computer vision problems, e.g.,estimation of pose, depth, size, object class, and properties such as“rough” vs. “smooth.”

Furthermore, embodiments of the system can quickly create large labeleddata sets of parts of systems managed by customers, and use the labeleddata sets to train computer vision systems. A trained system can assistservice technicians with management and repair of a part of a system,and can also allow a customer to assist an end-user with repair of asystem part (e.g., the Digital GloveBox and SmartScan applications). Adifferentiator between these existing tools and the proposed technologyis the large amount of time needed to collect the training data for thecomputer vision systems encased within the existing tools. This largeamount of time can be reduced to a tiny fraction (e.g., 1/10) by usingthe embodiments of the system to collect training data.

Other exemplary systems managed by customers can include: cars orvehicles (where the dashboard or other car part, e.g., an engine or afeature in the cabin of the car, may be a region of interest for whichthe customer may require assistance); and printers or other devices(where a feeder tray, output tray, control panel, or other part may bethe region of interest). A customer (or an end-user) who may requireassistance can take a photo of the system with his cell phone, andobtain useful information about a certain “labeled” section of thephoto. For example, if a user of a vehicle experiences an issue with thevehicle, the vehicle user can capture an image of the vehicle dashboardwith his mobile device, and, based on the previously generated diverseset of labeled images of the dashboard from various camera poses, thevehicle user can obtain a labeled image that may be used to assist theuser in understanding how to address the issue.

Embodiments of the system can also create deep vision-powered tools thatallow domain experts to easily create new, stand-alone, interactiveaugmented reality coaches without needing expertise in machine learningof 3D modeling tools. The system can include features related toauthoring, such as gestures for pointing out objects and regions,speech-to-text to provide labels, and object part segmentations. Thesystem can also include curation, such as storage, indexing andmetadata, and basic editing of clips. The system can further includefeatures related to assistance, such as part recognition, labelplacement, flow control, and part condition determination. The systemcan increase robustness and generalization of training throughbackground desensitivity, automatic lighting augmentation, and shadowgeneration. Furthermore, the system can include voice and activityrecognition to support interactive coaching, as well as applicationintegration and telemetry connections.

Thus, by using external tracking sensors to annotate data, and by usingaugmented reality/virtual reality (AR/VR) to collect annotated trainingdata for machine learning, the embodiments described herein can exploitthe capability of AR/VR to collect training data from multipleperspectives without requiring re-labeling for every new perspective ofa scene. This can result in a significant reduction in the burden oflabeling for training an effective computer vision system.

Exemplary Environment for Facilitating Efficient Collection of TrainingData

FIG. 1 illustrates an exemplary environment 100 for facilitatingefficient collection of training data, in accordance with an embodimentof the present invention. Environment 100 can include: a device 104 andan associated user 106; a device 108; a physical object 120; and sensors110. Device 104 can include an augmented reality device (such as aMicrosoft HoloLens). Physical object 120 can be part of a scene 121 (notshown) which has an associated 3D world coordinate frame. Device 108 caninclude a server or other computing device which can receive, transmit,and store data, and can perform an algorithm to project vertices intoimages taken from multiple perspectives in the 3D world coordinateframe. Sensors 110 and other tracking sensors (not shown) can worktogether with device 104 in a system to capture images, annotate images,determine 3D coordinates, store annotated images, project images, anddisplay projected images. Device 104, device 108, and sensors 110 cancommunicate via a network 102.

During operation, user 106 can use device 104 from a location 130 in the3D world coordinate frame to capture and annotate (function 132) animage 134 of physical object 120, and send image 134 (with user-createdannotations) to device 108. The annotated image can include multiplemarked vertices which are associated with 3D coordinates in the 3D worldcoordinate frame.

User 106 can use device 104 from a location 140 (which is different fromlocation 130) to capture (function 142) an image 144 of physical object120, and send image 144 to device 108. Device 108 can perform analgorithm to project the marked vertices from image 144 onto an image146, and send image 146 (with auto-created annotations) back to user106, to be displayed on device 104.

Furthermore, user 106 can use device 104 based on various scenecharacteristic changes 160 (e.g., other locations, different cameraposes, different lighting conditions, etc.), and transmit images 164 vianetwork 102 to device 108. Device 108 can perform the algorithm toproject the marked vertices (as identified or registered in image 144)onto images 166, and send images 166 (with auto-created annotations)back to user 106, to be displayed on device 104.

Device 108 can store data, such as: a world coordinate frame 150, whichcorresponds to scene 121 and describes an environment that includesphysical object 120; image 134 (with user-created annotations); image144, as captured by user 106; image 146 (with auto-created annotations);and images 166 (with auto-created annotations). Device 108 can alsostore, as a collection of data, training data 170, which can includeimages 134, 144, and 166.

Method for Facilitating Efficient Collection of Training Data

FIG. 2 presents a flow chart 200 illustrating a method for facilitatingefficient collection of training data, in accordance with an embodimentof the present invention. During operation, the system obtains, by arecording device, a first image of a physical object in a scene which isassociated with a three-dimensional (3D) world coordinate frame(operation 202). The system marks, on the first image, a plurality ofvertices associated with the physical object, wherein a vertex has 3Dcoordinates based on the 3D world coordinate frame (operation 204). Thesystem obtains a plurality of second images of the physical object inthe scene while changing one or more characteristics of the scene(operation 206). The system projects the marked vertices on to arespective second image to indicate a two-dimensional (2D) bounding areaassociated with the physical object (operation 208). The system stores,in a collection of training data, the first image with the markedplurality of vertices and the plurality of second images with theprojected marked vertices (operation 210). The system trains a neuralnetwork based on the collection of stored training data (operation 212).

Further Exemplary Embodiments: Annotating Multiple Regions of Interestat One Time; Annotating Subsequent to Capturing Images or Video;Annotating Volumes

The embodiments described herein can annotate multiple regions ofinterest at a time in a single image (or frame) (e.g., by markingmultiple plurality of vertices/points), such that a singleuser-annotated image with multiple marked pluralities of vertices canresult in the generation of images from different camera poses, whereeach image displays the annotated multiple regions of interest. That is,the system can label multiple regions of interest simultaneously.

Furthermore, the embodiments described herein allow a user to firstcapture many images, and then annotate one image, whereupon the systemcan automatically annotate the previously captured images based on theuser-annotated image. For example, assume that a user walks around anobject for 30 seconds and captures 100 images with a recording device orsystem. After walking for some period of time and capturing some images(e.g., after walking for seven seconds and capturing 24 images), theuser can label or mark an image (e.g., the 25th image), which can causethe system to automatically annotate both the previously captured 24images as well as the subsequently captured 75 images.

The embodiments described herein can also provide annotation of surfaceplanes by marking polygons, such as a shape which can indicate abounding box. The polygons can be either convex or non-convex. Marking anon-convex polygon may require additional information, and can be basedon, e.g., an order in which the points are marked. Furthermore,embodiments of the system can provide annotation of a volume, such asmarking a plurality of points to indicate a cube around the entireprinter itself. The system can project the entire volume onto subsequentimages or video frames which are automatically annotated based on theannotated volume.

Exemplary Demonstration of Method for Facilitating Efficient Collectionof Training Data

FIGS. 3, 4A-B, 5A-B, 6A-B, and 7A-C illustrate exemplary images forfacilitating efficient collection of training data. FIG. 3 illustrates ahuman using an AR device to mark corners of system features, inaccordance with an embodiment of the present invention. The environmentin FIG. 3 is a room with several objects, including a printer. The ARdevice can be a Microsoft HoloLens, a system which can define the 3Dworld coordinate frame for an environment (e.g., the room).

FIG. 4A illustrates an example of creating a bounding box of a printerfrom a first perspective using an AR device, in accordance with anembodiment of the present invention. The user can stand at a location inthe room and obtain an image. That is, the user can capture an image ofthe room, including the printer, from a certain perspective, where theperspective is based on the pose, location, orientation, etc., of the ARdevice in relation to the printer. Using the AR device, the user canplace green markers in a first image taken from this first perspective,and the system can display on this image a bounding area defined by thegreen markers. In FIG. 4A, the bounding area appears as box with a blueoutline around the printer. The system thus marks the plurality ofvertices associated with the physical object.

FIG. 4B illustrates an example of creating a bounding box of a printerfrom a second perspective using an AR device, in accordance with anembodiment of the present invention. In FIG. 4B, using the AR devicefrom a second perspective, the user can capture a second image of theroom. The second perspective is different from the first perspective ofFIG. 4A (i.e., where the AR device is in a different pose, location,orientation, etc. in relation to the printer). The system can project onto the second image the marked vertices as green markers, and alsoproject on to the second image a bounding area defined by the projectedgreen markers.

FIGS. 4A and 4B illustrate marking a single region of interest in ascene. As described above, a user can annotate multiple regions ofinterest at a time in a single frame by marking multiple pluralities ofvertices of a physical object. As such, a single user-annotated framewith multiple annotated regions of interest can result in the generationof images from different camera poses, where each image displays theannotated multiple regions of interest. That is, the system can labelmultiple regions of interest simultaneously.

FIG. 5A illustrates an example of creating a bounding box of printerparts from a first perspective using an AR device, in accordance with anembodiment of the present invention. Using the AR device, the user canplace green markers (not shown) on multiple regions of interest in afirst image taken from this first perspective, and the system candisplay on this image multiple bounding areas defined by the greenmarkers, e.g., a blue bounding box corresponding to the output tray, agreen bounding box corresponding to the control panel, and a redbounding box corresponding to the paper drawer. In some embodiments,FIG. 5A illustrates a display of the image based on previously markedvertices, such that the system displays on to this first image themultiple bounding boxes based on the previously marked vertices.

FIG. 5B illustrates an example of creating a bounding box of printerparts from a second perspective using an AR device, in accordance withan embodiment of the present invention. In FIG. 5B, using the AR devicefrom a second perspective, the user can capture a second image of theroom. The system can project on to this second image the markedplurality of vertices as green markers (not shown), and also project onto this second image the multiple bounding areas as defined by thepreviously marked plurality of vertices. Similar to FIG. 5A, a bluebounding box corresponds to the output tray, a green bounding boxcorresponds to the control panel, and a red bounding box corresponds tothe paper drawer.

FIG. 6A illustrates a labeling interface that can be used with MicrosoftHoloLens, in accordance with an embodiment of the present invention.Using the Microsoft HoloLens, the user can view control buttons (e.g.,the green oval and the blue circle) and surface meshes (e.g., whitepolygons) of the environment. The user can annotate the viewed image byplacing several virtual markers (green, red, and blue squares) usinghand movements while wearing the Microsoft HoloLens. The markers cancorrespond to various regions of interest associated with the printer.For example, the user can annotate that the green markers correspond toa paper drawer, the red markers correspond to a manual input area, andblue markers correspond to a control panel.

FIG. 6B illustrates the labeling interface of FIG. 6A without thesurface meshes, in accordance with an embodiment of the presentinvention.

FIGS. 7A-7C illustrate automatically generated marker locations andbounding boxes based on the annotations taken by the user in FIGS. 6Aand 6B. Note that while the color of the bounding areas in FIGS. 7A-7Care different than the corresponding bounding areas in FIG. 6A, thesystem displays the same previously annotated multiple regions ofinterest. In addition, the user can set the indicator of the boundingarea to any type, pattern, or color of a connector between the markerlocations.

FIG. 7A illustrates an example of automatically generatedtwo-dimensional marker locations and the corresponding bounding boxesfrom a first perspective using the Microsoft HoloLens interface, inaccordance with an embodiment of the present invention. In FIG. 7A, agreen bounding box corresponds to the control panel, a blue bounding boxcorresponds to the manual input area, and a red bounding box correspondsto the paper drawer.

FIG. 7B illustrates an example of automatically generatedtwo-dimensional marker locations and the corresponding bounding boxesfrom a second perspective using the Microsoft HoloLens interface, inaccordance with an embodiment of the present invention. In FIG. 7B, agreen bounding box corresponds to the control panel, a blue bounding boxcorresponds to the manual input area, and a red bounding box correspondsto the paper drawer.

FIG. 7C illustrates an example of automatically generatedtwo-dimensional marker locations and the corresponding bounding boxesfrom a second perspective using the Microsoft HoloLens interface, inaccordance with an embodiment of the present invention. In FIG. 7C, agreen bounding box corresponds to the control panel, a blue bounding boxcorresponds to the manual input area, and a red bounding box correspondsto the paper drawer.

Exemplary Computer and Communication System

FIG. 8 illustrates an exemplary computer and communication system 800that facilitates efficient collection of training data, in accordancewith an embodiment of the present invention. System 800 includes acomputer system 802 and a recording device 842, which can communicatevia a network (not shown). Computer system 802 and recording device 842can correspond, respectively, to device 108 and device 104 of FIG. 1.

Computer system 802 includes a processor 804, a memory 806, and astorage device 808. Memory 806 can include a volatile memory (e.g., RAM)that serves as a managed memory, and can be used to store one or morememory pools. Furthermore, computer system 802 can be coupled to adisplay device 810, a keyboard 812, and a pointing device 814. Storagedevice 808 can store an operating system 816, a content-processingsystem 818, and data 828.

Content-processing system 818 can include instructions, which whenexecuted by computer system 802, can cause computer system 802 toperform methods and/or processes described in this disclosure.Specifically, content-processing system 818 may include instructions forsending and/or receiving/obtaining data packets to/from other networknodes across a computer network (communication module 820). A datapacket can include an image, a video, a video frame, 3D coordinates of avertex, and information about a scene or a physical object in the scene.

Content-processing system 818 can include instructions for receiving afirst image of a physical object in a scene which is associated with a3D world coordinate frame, and for receiving a plurality of secondimages of the physical object in the scene based on one or more changedcharacteristics of the scene (communication module 820).Content-processing system 818 can include instructions for registering amarked plurality of vertices associated with the physical object(projection-determining module 822). Content-processing system 818 caninclude instructions for determining a projection of the marked verticeson to a respective second image (projection-determining module 822).Content-processing system 818 can include instructions for indicating a2D bounding area associated with the physical object (bounding areamanagement module 824). Content-processing system 818 can includeinstructions for storing the first image and the second images in acollection of training data, and for training a convolutional neuralnetwork to identify features of the physical object (network-trainingmodule 826).

Recording device 842 includes a processor 844, a memory 846, and astorage device 848. Memory 846 can include a volatile memory (e.g., RAM)that serves as a managed memory, and can be used to store one or morememory pools. Storage device 848 can store a content-processing system858 and data 868.

Content-processing system 858 can include instructions, which whenexecuted by recording device 842, can cause recording device 842 toperform methods and/or processes described in this disclosure.Specifically, content-processing system 858 may include instructions forsending and/or receiving/obtaining data packets to/from other networknodes across a computer network (communication module 860). A datapacket can include an image, a video, a video frame, 3D coordinates of avertex, and information about a scene or a physical object in the scene.

Content-processing system 858 can include instructions for obtaining afirst image of a physical object in a scene which is associated with athree-dimensional (3D) world coordinate frame (image-obtaining module862). Content-processing system 858 can include instructions formarking, on the first image, a plurality of vertices associated with thephysical object, wherein a vertex has 3D coordinates based on the 3Dworld coordinate frame (vertex-marking module 864). Content-processingsystem 858 can include instructions for obtaining a plurality of secondimages of the physical object in the scene while changing one or morecharacteristics of the scene (image-obtaining module 862).Content-processing system 858 can include instructions for projectingthe marked vertices on to a respective second image to indicate atwo-dimensional (2D) bounding area associated with the physical object(image-displaying module 866).

Data 828 and 868 can include any data that is required as input or thatis generated as output by the methods and/or processes described in thisdisclosure. Specifically, data 828 and 868 can store at least: data; animage; an image of a physical object; a 3D world coordinate frame; avertex; 3D coordinates for a vertex; a scene; a characteristic of thescene; an indicator of a region of interest on a physical object; markedvertices; a projection of the marked vertices; a polygon; a portion of asurface plane; a volume; a 2D shape; a 3D volume; a 2D bounding area; anannotation; a label; a type, pattern, or color of a connector betweenprojected vertices in an image; a color, shading, or fill of a shapeformed by connecting projected vertices in an image; text describing a2D bounding area; an indication of a label or description for a 2Dbounding area; a pose of the recording device; a lighting of the scene;a distance of the recording device from the physical object; anorientation of the recording device in relation to the physical object;a background of the physical object or the scene; an occlusion of one ormore portions of the physical object; a collection of training data; atrained network; an image with user-created annotations; and an imagewith system-created or automatically generated annotations.

The data structures and code described in this detailed description aretypically stored on a computer-readable storage medium, which may be anydevice or medium that can store code and/or data for use by a computersystem. The computer-readable storage medium includes, but is notlimited to, volatile memory, non-volatile memory, magnetic and opticalstorage devices such as disk drives, magnetic tape, CDs (compact discs),DVDs (digital versatile discs or digital video discs), or other mediacapable of storing computer-readable media now known or later developed.

The methods and processes described in the detailed description sectioncan be embodied as code and/or data, which can be stored in acomputer-readable storage medium as described above. When a computersystem reads and executes the code and/or data stored on thecomputer-readable storage medium, the computer system performs themethods and processes embodied as data structures and code and storedwithin the computer-readable storage medium.

Furthermore, the methods and processes described above can be includedin hardware modules or apparatus. The hardware modules or apparatus caninclude, but are not limited to, application-specific integrated circuit(ASIC) chips, field-programmable gate arrays (FPGAs), dedicated orshared processors that execute a particular software module or a pieceof code at a particular time, and other programmable-logic devices nowknown or later developed. When the hardware modules or apparatus areactivated, they perform the methods and processes included within them.

The foregoing descriptions of embodiments of the present invention havebeen presented for purposes of illustration and description only. Theyare not intended to be exhaustive or to limit the present invention tothe forms disclosed. Accordingly, many modifications and variations willbe apparent to practitioners skilled in the art. Additionally, the abovedisclosure is not intended to limit the present invention. The scope ofthe present invention is defined by the appended claims.

What is claimed is:
 1. A computer-implemented method for facilitatingefficient collection of training data, the method comprising: obtaining,by a recording device, a first image of a physical object in a scenewhich is associated with a three-dimensional (3D) world coordinateframe; marking, on the first image, a plurality of vertices associatedwith the physical object, wherein a vertex has 3D coordinates based onthe 3D world coordinate frame; obtaining a plurality of second images ofthe physical object in the scene while changing one or morecharacteristics of the scene; and projecting the marked vertices on to arespective second image to indicate a two-dimensional (2D) bounding areaassociated with the physical object.
 2. The method of claim 1, whereinthe marked plurality of vertices corresponds to one or more regions ofinterest on the physical object, and wherein projecting the markedvertices further comprising indicating a 2D bounding area associatedwith the one or more regions of interest on the physical object.
 3. Themethod of claim 1, wherein the marked plurality of vertices can indicateone or more of: a polygon; a portion of a surface plane; and a volume.4. The method of claim 1, wherein marking the plurality of verticesfurther comprises: determining how to indicate the 2D bounding area ofthe projected marked vertices on the respective second image.
 5. Themethod of claim 1, wherein the 2D bounding area and the respectivesecond image are presented on a display associated with the recordingdevice, and wherein the 2D bounding area indicates a 2D shape or a 3Dvolume.
 6. The method of claim 1, wherein the 2D bounding area isindicated by one or more of: a type, pattern, or color of a connectorbetween the projected vertices in the respective second image; a color,shading, or fill of a shape formed by connecting the projected verticesin the respective second image; text describing the 2D bounding area;and an indication of a label or description for the 2D bounding area. 7.The method of claim 1, wherein a 2D bounding area corresponds to acharacteristic of the scene.
 8. The method of claim 1, wherein acharacteristic of the scene is one or more of: a pose of the recordingdevice; a lighting of the scene; a distance of the recording device fromthe physical object; an orientation of the recording device in relationto the physical object; a background of the physical object or thescene; and an occlusion of one or more portions of the physical object.9. The method of claim 1, further comprising: storing, in a collectionof training data, the first image with the marked plurality of vertices;storing, in the collection of training data, the plurality of secondimages with the projected marked vertices; training, based on thecollection of training data, a convolutional neural network to identifyfeatures of the physical object; and deploying the trained network on amobile computing device to identify the features of the physical object.10. The method of claim 9, further comprising: training, based on thecollection of training data, a computer vision system; providing, by thetrained computer vision system, information to a user to manage orrepair a first part or a first component of the computer vision system;and providing, by the trained computer vision system, information to auser to guide an end-user with repair of a second part or a secondcomponent of the computer vision system.
 11. The method of claim 10,wherein the computer vision system is used in a field which involves oneor more of: cars or vehicles; printers or other devices; authoringobjects; and curating objects, including one or more of storing data,indexing data and associated metadata, and editing data.
 12. The methodof claim 10, wherein managing or repairing a part or a component of acomputer vision system is based on one or more of: part recognition,label placement, flow control, and part condition determination;background desensitivity, automatic lighting augmentation, and shadowgeneration; voice recognition or activity recognition; and interactivecoaching, application integration, and telemetry connections.
 13. Themethod of claim 1, wherein the recording device includes one or more of:an augmented reality device; a virtual reality device; a device withmagnetic sensors which determine 3D coordinates for a vertex in the 3Dworld coordinate frame; a camera and a hand-tracking sensor; a camerawhich records red, green, and blue (RGB), wherein the hand-trackingsensor determines 3D coordinates for a vertex in the 3D world coordinateframe; a camera which records red, green, and blue (RGB), and a 3Dsensor which records a depth; a device which records images or video,and determines 3D coordinates for a vertex in the 3D world coordinateframe based on visual cues or a position-sensing technology; and adevice which records images or video and includes a (3D) sensor.
 14. Themethod of claim 1, further comprising: displaying, by the recordingdevice on a display of the recording device, a respective second imageof the physical object in the scene.
 15. The method of claim 14, whereindisplaying the respective second image of the physical object in thescene involves: projecting, on the display, the marked plurality ofvertices associated with the physical object onto the respective secondimage; and indicating, on the display, a two-dimensional (2D) boundingarea which is associated with the physical object and includes themarked vertices.
 16. The method of claim 1, further comprising:obtaining, by the recording device, 3D information associated with alocation, orientation, or pose of the physical object; and applying theobtained 3D information to estimate a pose, depth, size, object class,or property associated with the physical object.
 17. The method of claim16, wherein obtaining the 3D information is based on the first image andthe plurality of second images.